Land cover analysis has received significant attention in remote sensing-related fields. To take advantage of multi-modal data, Hyperspectral Images (HSI) and Light Detection and Ranging (LiDAR) are often combined. However, it is difficult to capture intricate local and global spectral-spatial associations between HSI and LiDAR. To exploit the complementary information of multi-modal data, a Spectral-Spatial Graph Network is proposed that integrates HSI and LiDAR data into intricate local and global spectral-spatial associations. Specifically, the network consists of a local module and a global module. The local module uses convolution techniques applied over image patches to preserve the local spatial relationships available in multi-modal data. The global module constructs a spectral-spatial multi-modal graph, which is used to preserve spectral-spatial proximity in multi-modal data. Both the local and global modules are utilized to their utmost capacity to generate the final multi-modal data representation. Experiments on multi-modal remote sensing data sets reveal that the proposed network has attained performance levels comparable to state-of-the-art methods. Author